Spatio-Temporal triangular-chain CRF for activity recognition

MM 2015 - Proceedings of the 2015 ACM Multimedia Conference

Volumn , Issue , 2015, Pages 1151-1154

  Cao, Congqi ^a   Zhang, Yifan ^a   Lu, Hanqing ^a  

^a INSTITUTE OF AUTOMATION   (China)

Author keywords

Activity recognition; Hierarchical model; Joint learning; Spatiotemporal dependencies; Triangular chain CRFs

Indexed keywords

CHAINS; COMPUTER VISION; HIERARCHICAL SYSTEMS;

ACTIVITY RECOGNITION; HIERARCHICAL MODEL; JOINT LEARNING; SPATIO-TEMPORAL DEPENDENCIES; TRIANGULAR CHAINS;

PATTERN RECOGNITION;

EID: 84962792182     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2733373.2806304     Document Type: Conference Paper

References (16)

1. J. Besag. Statistical analysis of non-lattice data. Journal of the Royal Statistical Society. Series D (The Statistician), 24(3):pp. 179-195, 1975.
2. C. Chen, Y. Zhuang, F. Nie, Y. Yang, F. Wu, and J. Xiao. Learning a 3d human pose distance metric from geometric pose descriptor. IEEE Trans. Vis. Comput. Graph., 17(11):1676-1689, 2011.
3. H. Chu, W. Lin, J. Wu, X. Zhou, Y. Chen, and H. Li. A new heat-map-based algorithm for human group activity recognition. In Proceedings of the 20th ACM Multimedia Conference, October 29-November 02, 2012, pages 1069-1072. ACM, 2012.
4. A. Fathi and J. M. Rehg. Modeling actions through state changes. In CVPR 2013, June 23-28, 2013, pages 2579-2586. IEEE, 2013.
5. M. Jeong and G. G. Lee. Triangular-chain conditional random fields. IEEE Transactions on Audio, Speech & Language Processing, 16(7):1287-1302, 2008.
6. M. Jeong and G. G. Lee. Multi-domain spoken language understanding with transfer learning. Speech Communication, 51(5):412-424, 2009.
7. H. Kuehne, A. B. Arslan, and T. Serre. The language of actions: Recovering the syntax and semantics of goal-directed human activities. In CVPR 2014, June 23-28, 2014, pages 780-787. IEEE, 2014.
8. J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML 2001, June 28-July 1, 2001, pages 282-289. Morgan Kaufmann, 2001.
9. I. Lillo, A. Soto, and J. C. Niebles. Discriminative hierarchical modeling of spatio-Temporally composable human activities. In CVPR 2014, June 23-28, 2014, pages 812-819, 2014.
10. J. Shotton, A. W. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake. Real-Time human pose recognition in parts from single depth images. In CVPR 2011, 20-25 June 2011, pages 1297-1304. IEEE Computer Society, 2011.
11. C. Sminchisescu, A. Kanaujia, and D. N. Metaxas. Conditional models for contextual human motion recognition. Computer Vision and Image Understanding, 104(2-3):210-220, 2006.
12. D. Tran, L. D. Bourdev, R. Fergus, L. Torresani, and M. Paluri. C3D: generic features for video analysis. CoRR, abs/1412.0767, 2014.
13. N. N. Vo and A. F. Bobick. From stochastic grammar to bayes network: Probabilistic parsing of complex activity. In CVPR 2014, June 23-28, 2014, pages 2641-2648. IEEE, 2014.
14. P. Xu and R. Sarikaya. Convolutional neural network based triangular CRF for joint intent detection and slot filling. In 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, December 8-12, 2013, pages 78-83. IEEE, 2013.
15. Z. Xu, Y. Yang, and A. G. Hauptmann. A discriminative CNN video representation for event detection. CoRR, abs/1411.4006, 2014.
16. Y. Zhang, Y. Zhang, E. Swears, N. Larios, Z. Wang, and Q. Ji. Modeling temporal interactions with interval temporal Bayesian networks for complex activity recognition. IEEE Trans. Pattern Anal. Mach. Intell., 35(10):2468-2483, 2013.